Abstract 5411: Association of treatment response, recurrence and underlying molecular mechanism for over 22 TCGA cancer types
Authors: Jianfang Liu, Peter T. Hu, Praveen-Kumar Raj-Kumar, Anupama Praveen-Kumar, Xiaoying Lin, Craig D. Shriver, Hai Hu
Journal: Cancer Research (2022)

Abstract Introduction: The treatment methods and response vary among different cancer types that can result in different cancer recurrent rate and clinical outcomes, within and between cancer types. Here we performed a comprehensive analysis on TCGA (The Cancer Genome Atlas) treatment data over 22 cancer types, to explore the efficacy of cancer treatments, the recurrent rates of each cancer type, and their associated underlying molecular mechanism. Methods: TCGA treatment data for 22 cancer types was downloaded from TCGA data portal, and processed. Treatment response rate and recurrent rate across different cancer types were compared. Survival analysis was performed between patients who achieved complete response after first course of treatment (CRAFCT) with those who did not. RNAseq data analyses were further performed to identify significant genes associated with CRAFCT vs without CRAFCT to find the underlying molecular reasons of treatment efficacy. Results: Treatment responses after first course of treatment were compared for 22 cancer types. Cancer types were classified based on the rate of CRAFCT into 3 categories: high (&amp;gt;80%), median (50%-80%), and low (&amp;lt;50%); There were 12 cancer types with high complete response rate, 8 with median complete response rate, and 2 with low complete response rate. Cancer recurrent rates were further classified as high (&amp;gt;25%), median (10%-25%), and low (&amp;lt;10%) to check how the complete response rates affect the recurrent rate. After achieving CRAFCT, we found that cancers having high response rate can have low, median and high recurrent rate, while cancers having median or low response rate can have median and high recurrent rate. Patients who achieved CRAFCT had a much better overall survival than patients who didn’t across all 22 cancer types. Significantly expressed genes associated with treatment efficacy were identified and compared across 18 out of 22 cancer types. 6 genes were shared over 4 cancer types, 87 genes were shared over 3 cancer types. Finally, gene and cancer type networks were constructed to demonstrate the connections of cancer types, genes, and relationships of cancer types and genes. Conclusion: Cancer treatment therapy and therapy regimens were either cancer-specific or shared across cancer types; different cancer types had different treatment response rate which might result in different cancer recurrent rate; treatment related gene-cancer networks were constructed across cancer types which could be targeted for clinical and therapeutic applications to drive new drug development for multiple cancer types. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views, opinions or policies of the USUHS, HJF, the DoD or the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Jianfang Liu, Peter T. Hu, Praveen-Kumar Raj-Kumar, Anupama Praveen-Kumar, Xiaoying Lin, Craig D. Shriver, Hai Hu. Association of treatment response, recurrence and underlying molecular mechanism for over 22 TCGA cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5411.